Sim.DiffProc (version 2.5)

Ajdt: Adjustment By Student t Distribution

Description

Adjusted your sample by the student t law, estimated these parameters using the method of maximum likelihood, and calculating the Akaike information criterion for one or several fitted model objects for which a log-likelihood value can be obtained, according to the formula -2*log-likelihood + k*npar, where npar represents the number of parameters in the fitted model, and k = 2 for the usual AIC, and computes confidence intervals for one or more parameters in a fitted model (Law).

Usage

Ajdt(X, starts = list(df = 1), leve = 0.95)

Arguments

X

a numeric vector of the observed values.

starts

named list. Initial values for optimizer.

leve

the confidence level required.

Value

• coef
  Coefficients extracted from the model.
• AIC
  A numeric value with the corresponding AIC.
• vcov
  A matrix of the estimated covariances between the parameter estimates in the linear or non-linear predictor of the model.
• confint
  A matrix (or vector) with columns giving lower and upper confidence limits for each parameter. These will be labelled as (1-level)/2 and 1 - (1-level)/2.

Details

The optim optimizer is used to find the minimum of the negative log-likelihood. An approximate covariance matrix for the parameters is obtained by inverting the Hessian matrix at the optimum. For more detail consulted mle,confint,AIC. R has the [dqpr]t functions to evaluate the density, the quantiles, and the cumulative distribution or generate pseudo random numbers from the student t distribution.

See Also

Ajdchisq Adjustment By Chi-Squared Distribution,Ajdexp Adjustment By Exponential Distribution, Ajdf Adjustment By F Distribution,Ajdgamma Adjustment By Gamma Distribution, Ajdlognorm Adjustment By Log Normal Distribution,Ajdnorm Adjustment By Normal Distribution, Ajdweibull Adjustment By Weibull Distribution,Ajdbeta Adjustment By Beta Distribution.

Examples

X <- rt(1000,df=2)
Ajdt(X, starts = list(df = 1), leve = 0.95)